Immunoinformatics: Predicting Immunogenicity In Silico

Author:   Darren R. Flower
Publisher:   Humana Press Inc.
Edition:   Softcover reprint of hardcover 1st ed. 2007
Volume:   409
ISBN:  

9781617377259


Pages:   438
Publication Date:   19 November 2010
Format:   Paperback
Availability:   In Print   Availability explained
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Immunoinformatics: Predicting Immunogenicity In Silico


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Overview

Immunoinformatics: Predicting Immunogenicity In Silico is a primer for researchers interested in this emerging and exciting technology and provides examples in the major areas within the field of immunoinformatics. This volume both engages the reader and provides a sound foundation for the use of immunoinformatics techniques in immunology and vaccinology. The volume is conveniently divided into four sections. The first section, Databases, details various immunoinformatic databases, including IMGT/HLA, IPD, and SYEPEITHI. In the second section, Defining HLA Supertypes, authors discuss supertypes of GRID/CPCA and hierarchical clustering methods, Hla-Ad supertypes, MHC supertypes, and Class I Hla Alleles. The third section, Predicting Peptide-MCH Binding, includes discussions of MCH binders, T-Cell epitopes, Class I and II Mouse Major Histocompatibility, and HLA-peptide binding. Within the fourth section, Predicting Other Properties of Immune Systems, investigators outline TAP binding, B-cell epitopes, MHC similarities, and predicting virulence factors of immunological interest. Immunoinformatics: Predicting Immunogenicity In Silico merges skill sets of the lab-based and the computer-based science professional into one easy-to-use, insightful volume.

Full Product Details

Author:   Darren R. Flower
Publisher:   Humana Press Inc.
Imprint:   Humana Press Inc.
Edition:   Softcover reprint of hardcover 1st ed. 2007
Volume:   409
Dimensions:   Width: 15.20cm , Height: 2.60cm , Length: 22.90cm
Weight:   0.671kg
ISBN:  

9781617377259


ISBN 10:   1617377252
Pages:   438
Publication Date:   19 November 2010
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

Databases.- IMGT®, the International ImmunoGeneTics Information System® for Immunoinformatics.- The IMGT/HLA Database.- IPD.- SYFPEITHI.- Searching and Mapping of T-Cell Epitopes, MHC Binders, and TAP Binders.- Searching and Mapping of B-Cell Epitopes in Bcipep Database.- Searching Haptens, Carrier Proteins, and Anti-Hapten Antibodies.- Defining HLA Supertypes.- The Classification of HLA Supertypes by GRID/CPCA and Hierarchical Clustering Methods.- Structural Basis for HLA-A2 Supertypes.- Definition of MHC Supertypes Through Clustering of MHC Peptide-Binding Repertoires.- Grouping of Class I HLA Alleles Using Electrostatic Distribution Maps of the Peptide Binding Grooves.- Predicting Peptide-MHC Binding.- Prediction of Peptide-MHC Binding Using Profiles.- Application of Machine Learning Techniques in Predicting MHC Binders.- Artificial Intelligence Methods for Predicting T-Cell Epitopes.- Toward the Prediction of Class I and II Mouse Major Histocompatibility Complex-Peptide-Binding Affinity.- Predicting the MHC-Peptide Affinity Using Some Interactive-Type Molecular Descriptors and QSAR Models.- Implementing the Modular MHC Model for Predicting Peptide Binding.- Support Vector Machine-Based Prediction of MHC-Binding Peptides.- In Silico Prediction of Peptide-MHC Binding Affinity Using SVRMHC.- HLA-Peptide Binding Prediction Using Structural and Modeling Principles.- A Practical Guide to Structure-Based Prediction of MHC-Binding Peptides.- Static Energy Analysis of MHC Class I and Class II Peptide-Binding Affinity.- Molecular Dynamics Simulations.- An Iterative Approach to Class II Predictions.- Building a Meta-Predictor for MHC Class II-Binding Peptides.- Nonlinear Predictive Modeling of MHC Class II-Peptide Binding Using Bayesian Neural Networks.- Predicting otherProperties of Immune Systems.- TAPPred Prediction of TAP-Binding Peptides in Antigens.- Prediction Methods for B-cell Epitopes.- HistoCheck.- Predicting Virulence Factors of Immunological Interest.- Immunoinformatics and the in Silico Prediction of Immunogenicity.- Immunoinformatics and the in Silico Prediction of Immunogenicity.

Reviews

Investigators considering problems of recombinant vaccine design, possible host responses, and how to select likely sites from a large pool of information (the protein of interest) will find valuable material here. -Doody's Book Review, Weighted Numerical Score:77 - 3 Stars ...a value to virtually any investigator in this general field. -Doody's Book Review, Weighted Numerical Score:77 - 3 Stars ...a valuable addition to libraries in universities and research institutes, R & D firms engaged in the development of vaccines and immunotherapeutics, and clinical research centres. -Immunology news


Investigators considering problems of recombinant vaccine design, possible host responses, and how to select likely sites from a large pool of information (the protein of interest) will find valuable material here. -Doody's Book Review, Weighted Numerical Score:77 - 3 Stars ...a value to virtually any investigator in this general field. -Doody's Book Review, Weighted Numerical Score:77 - 3 Stars ...a valuable addition to libraries in universities and research institutes, R & D firms engaged in the development of vaccines and immunotherapeutics, and clinical research centres. -Immunology news


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